【摘要】 低分辨率是影响人脸识别精度的重要因素。一种有效方法是使用图像超分辨率技术对低分辨率图像重建，生成超分辨率图像后再对其作人脸识别，从而克服低分辨率面部图像对人脸识别的限制。但是，现有超分辨率方法在重建过程中往往忽略了保持其原始身份信息，这直接影响生成图像的人脸识别结果。针对上述问题，提出了一种身份保持约束下的面部超分辨率重建方法 IPNet，在提高低分辨率面部图像质量的同时，能保持重建后的面部图像身份。IPNet方法将语义分割网络和面部生成器相结合，通过语义分割网络提取低维隐码和多分辨率空间特征，进而指导面部生成器输出接近于原图的真实面部图像。在此基础上引入人脸识别网络，将身份信息整合到超分辨率方法中，从而约束重建前后的面部图像身份保持一致。实验结果表明，IPNet方法在超分辨率图像质量和身份保持上均优于其他对比方法。更多还原

【Abstract】 Low resolution is an important factor that affects the accuracy of face recognition. To overcome the limitation of low-resolution facial images on face recognition, one effective solution is adopting superresolution methods to reconstruct low-resolution images and then identify the generated facial images.However, existing super-resolution methods typically fail to consider facial identity preservation during reconstruction, which directly results in poor face recognition performance of reconstructed images. To address the issue mentioned above, this paper proposes a face super-resolution reconstruction method with identity preserving, called IPNet. This method can simultaneously improve the quality of low-resolution facial images and preserve the identity of reconstructed images. IPNet consists of a semantic segmentation network and a face generator. The semantic segmentation network is introduced to extract low-dimensional latent code and multi-resolution spatial features. Then, the extracted features guide the face generator to output super-resolution images similar to the authentic images. Furthermore, we introduce the face recognition network to integrate the face identity information into the super-resolution model, thus maintaining the identity of reconstructed facial images consistent with original images. Experimental results show that IPNet achieves better results than other comparison methods in terms of both super-resolution image quality and identity preservation, demonstrating effectiveness of the proposed method.更多还原